Faculty Profiles - TAKEDA, Kazuya

Papers - TAKEDA, Kazuya

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Prediction method for continuous point cloud data compression using SLAM information

Chenxi Tu, Eijiro Takeuchi, Chiyomi Miyajima, and Kazuya Takeda

4th International Symposium on Future Active Safety Technology toward zero traffic accidents (FAST-zero '17) page： 00 2017.9

Speaker-dependent WaveNet vocoder

Akira Tamamori,Tomoki Hayashi,Kazuhiro Kobayashi,Kazuya Takeda,Tomoki Toda,

INTERSPEECH 2017 page： pp. 1118-1122 2017.8

Stereophonic Music Separation Based on Non-negative Tensor Factorization with Cepstrum Regularization

Shogo Seki, Tomoki Toda, Kazuya Takeda

EUSIPCO2017 page： pp. 1011-1015 2017.8

Stereophonic Music Separation Based on Non-negative Tensor Factorization with Cepstrum Regularization

Seki Shogo, Toda Tomoki, Takeda Kazuya

2017 25TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO) page： 981-985 2017.8

Open Source Integrated Planner for Autonomous Navigation in Highly Dynamic Environments

Darweesh Hatem, Takeuchi Eijiro, Takeda Kazuya, Ninomiya Yoshiki, Sujiwo Adi, Morales Luis Yoichi, Akai Naoki, Tomizawa Tetsuo, Kato Shinpei

JOURNAL OF ROBOTICS AND MECHATRONICS Vol. 29 ( 4 ) page： 668-684 2017.8

Continuous point cloud data compression using SLAM based prediction Reviewed

Chenxi Tu, Eijro Takeuchi, Chiyomi Miyajima, Kazuya Takeda

IEEE 2017 Intelligent Vehicles Symposium (IV '17) page： 1744–1751 2017.6

Music Staging AI

Kenta Niwa, Kento Ohtani, Kazuya Takeda

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2017) page： 6588-6589 2017.5

BLSTM-HMM hybrid system combined with sound activity detection network for polyphonic sound event detection Reviewed

T. Hayashi, S. Watanabe, T. Toda, T. Hori, J. Le Roux, K. Takeda.

Proc. ICASSP page： 766-770 2017.3

Signal Processing for Smart Vehicle Technologies: Part 2 [From the Guest Editors] Reviewed

John H.L. Hansen, Kazuya Takeda, Sanjeev M. Naik, Mohan M. Trivedi, Gerhard U. Schmidt, Yingying Jennifer Chen, Wade Trappe

IEEE Signal Processing Magazine Vol. 34 ( 2 ) page： 18 - 21 2017.3

MUSIC STAGING AI Reviewed

Niwa Kenta, Ohtani Kento, Takeda Kazuya

2017 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) page： 6588-6589 2017

AN INVESTIGATION OF MULTI-SPEAKER TRAINING FORWAVENET VOCODER Reviewed

Hayashi Tomoki, Tamamori Akira, Kobayashi Kazuhiro, Takeda Kazuya, Toda Tomoki

2017 IEEE AUTOMATIC SPEECH RECOGNITION AND UNDERSTANDING WORKSHOP (ASRU) page： 712-718 2017

BLSTM-HMM HYBRID SYSTEM COMBINED WITH SOUND ACTIVITY DETECTION NETWORK FOR POLYPHONIC SOUND EVENT DETECTION Reviewed

Hayashi Tomoki, Watanabe Shinji, Toda Tomoki, Hori Takaaki, Le Roux Jonathan, Takeda Kazuya

2017 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) page： 766-770 2017

Continuous Point Cloud Data Compression Using SLAM Based Prediction Reviewed

Tu Chenxi, Takeuchi Eijiro, Miyajima Chiyomi, Takeda Kazuya

2017 28TH IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV 2017) page： 1744-1751 2017

MISSING COMPONENT RESTORATION FOR MASKED SPEECH SIGNALS BASED ON TIME-DOMAIN SPECTROGRAM FACTORIZATION Reviewed

Seki Shogo, Kameoka Hirokazu, Toda Tomoki, Takeda Kazuya

2017 IEEE 27TH INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING page： . 2017

Speaker-dependent WaveNet vocoder Reviewed

Akira Tamamori, Tomoki Hayashi, Kazuhiro Kobayashi, Kazuya Takeda, Tomoki Toda

Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH Vol. 2017-August page： 1118 - 1122 2017

Language：Japanese Publishing type：Research paper (international conference proceedings) Publisher：ISCA-INT SPEECH COMMUNICATION ASSOC

Copyright © 2017 ISCA. In this study, we propose a speaker-dependent WaveNet vocoder, a method of synthesizing speech waveforms with WaveNet, by utilizing acoustic features from existing vocoder as auxiliary features of WaveNet. It is expected that WaveNet can learn a sample-by-sample correspondence between speech waveform and acoustic features. The advantage of the proposed method is that it does not require (1) explicit modeling of excitation signals and (2) various assumptions, which are based on prior knowledge specific to speech. We conducted both subjective and objective evaluation experiments on CMU-ARCTIC database. From the results of the objective evaluation, it was demonstrated that the proposed method could generate high-quality speech with phase information recovered, which was lost by a mel-cepstrum vocoder. From the results of the subjective evaluation, it was demonstrated that the sound quality of the proposed method was significantly improved from mel-cepstrum vocoder, and the proposed method could capture source excitation information more accurately.

DOI： 10.21437/Interspeech.2017-314

Web of Science

Scopus

Impact of acoustic similarity on efficiency of verbal information transmission via subtle prosodic cues Reviewed

Bohan Chen, Norihide Kitaoka, Kazuya Takeda

Eurasip Journal on Audio, Speech, and Music Processing Vol. 2016 ( 1 ) 2016.12

Language：English Publishing type：Research paper (scientific journal) Publisher：SPRINGER INTERNATIONAL PUBLISHING AG

© 2016, The Author(s). In this study, we investigate the effect of tiny acoustic differences on the efficiency of prosodic information transmission. Study participants listened to textually ambiguous sentences, which could be understood with prosodic cues, such as syllable length and pause length. Sentences were uttered in voices similar to the participant’s own voice and in voices dissimilar to their own voice. The participants then identified which of four pictures the speaker was referring to. Both the eye movement and response time of the participants were recorded. Eye tracking and response time results both showed that participants understood the textually ambiguous sentences faster when listening to voices similar to their own. The results also suggest that tiny acoustic features, which do not contain verbal meaning can influence the processing of verbal information.

DOI： 10.1186/s13636-016-0097-6

Web of Science

Scopus

Investigation on Recurrent Neural Network Architectures for Daily Activity Recognition Reviewed

Akira Tamamori, Tomoki Hayashi, Tomoki Toda, Kazuya Takeda

The 3rd International Conference on Universal Village (UV2016) page： 00 2016.12

Sound enhancement system using selective binary filtering Reviewed

Tomomi Suzuki, Takanori Nishino, Yoshio Ishiguro, Kazuya Takeda

5th Joint Meeting of the ASA and ASJ page： 00 2016.12

Convolutional Biderectional LSTM-HMM Hybrid System for Polyphonic Sound Event Detection Reviewed

Tomoki Hayashi, Shinji Watanabe, Tomoki Toda, Takaaki Hori, Jonathan Le Roux, Kazuya Takeda

5th Joint Meeting of the ASA and ASJ page： 00 2016.12

Signal Processing for Smart Vehicle Technologies [From the Guest Editors]

John H.L. Hansen, Kazuya Takeda, Sanjeev M. Naik, Mohan M. Trivedi, Gerhard U. Schmidt, Yingying Chen

IEEE Signal Processing Magazine Vol. 33 ( 6 ) page： 12 - 13 2016.11

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Papers - TAKEDA, Kazuya